Construction of surrogate model by machine learning using FEM analysis results of ground improved by Deep Mixing

Abstract

When ground improvement methods, such as Deep Mixing, are applied to the foundation of port and harbor structures, it is necessary to conduct a seismic response analysis under the level 2 earthquake ground motion. The level 1 reliability-based design cannot directly take into account for spatial variability, so the average unconfined compression strength of in-situ improved ground is multiplied by various coefficients to reduce it to simulate spatial variability. In contrast, the level 3 reliability-based design can directly take into account for this spatial variability, enabling a more rational design from the perspectives of structural stability and economic efficiency. However, this design method requires Monte Carlo simulation, and the analysis cost is very high when applied to FEM. In this study, we investigated the construction of a surrogate model by machine learning to reduce the analysis cost.